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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 10, issue 8
Geosci. Model Dev., 10, 2905-2923, 2017
https://doi.org/10.5194/gmd-10-2905-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 10, 2905-2923, 2017
https://doi.org/10.5194/gmd-10-2905-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 01 Aug 2017

Development and technical paper | 01 Aug 2017

REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses

Bin Cao1,2, Stephan Gruber2, and Tingjun Zhang1 Bin Cao et al.
  • 1Key Laboratory of Western China's Environmental Systems (MOE), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • 2Department of Geography & Environmental Studies, Carleton University, Ottawa, K1S 5B6, Canada

Abstract. In mountain areas, the use of coarse-grid reanalysis data for driving fine-scale models requires downscaling of near-surface (e.g., 2m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e., the difference between the simulated 2m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially variable land-surface correction factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation.

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To derive the air temperature in mountain enviroments, we propose a new downscaling method with a spatially variable magnitude of surface effects. Our findings suggest that the difference between near-surface air temperature and upper-air temerpature is a good proxy of surface effects. It can be used to improve downscaling results, especially in valleys with strong surface effects and cold air pooling during winter.
To derive the air temperature in mountain enviroments, we propose a new downscaling method with...
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